{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 教程网页版：\n",
    "https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Training a Classifier\n",
    "=====================\n",
    "\n",
    "This is it. You have seen how to define neural networks, compute loss and make\n",
    "updates to the weights of the network.\n",
    "\n",
    "Now you might be thinking,\n",
    "\n",
    "What about data?\n",
    "----------------\n",
    "\n",
    "Generally, when you have to deal with image, text, audio or video data,\n",
    "you can use standard python packages that load data into a numpy array.\n",
    "Then you can convert this array into a ``torch.*Tensor``.\n",
    "\n",
    "-  For images, packages such as Pillow, OpenCV are useful\n",
    "-  For audio, packages such as scipy and librosa\n",
    "-  For text, either raw Python or Cython based loading, or NLTK and\n",
    "   SpaCy are useful\n",
    "\n",
    "Specifically for vision, we have created a package called\n",
    "``torchvision``, that has data loaders for common datasets such as\n",
    "Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
    "``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
    "\n",
    "This provides a huge convenience and avoids writing boilerplate code.\n",
    "\n",
    "For this tutorial, we will use the CIFAR10 dataset.\n",
    "It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
    "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
    "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
    "\n",
    ".. figure:: /_static/img/cifar10.png\n",
    "   :alt: cifar10\n",
    "\n",
    "   cifar10\n",
    "\n",
    "\n",
    "Training an image classifier\n",
    "----------------------------\n",
    "\n",
    "We will do the following steps in order:\n",
    "\n",
    "1. Load and normalize the CIFAR10 training and test datasets using\n",
    "   ``torchvision``\n",
    "2. Define a Convolutional Neural Network\n",
    "3. Define a loss function\n",
    "4. Train the network on the training data\n",
    "5. Test the network on the test data\n",
    "\n",
    "1. Load and normalize CIFAR10\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "Using ``torchvision``, it’s extremely easy to load CIFAR10.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of torchvision datasets are PILImage images of range [0, 1].\n",
    "We transform them to Tensors of normalized range [-1, 1].\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
    "    the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这一上来就搞一个transforms，让人很迷糊。这里贴一下这个包的功能：\n",
    "\n",
    "Transforms are common **image transformations**. They can be chained together using `Compose`. Most transform classes have a function equivalent: functional transforms give fine-grained control over the transformations. This is useful if you have to build a more complex transformation **pipeline** (e.g. in the case of segmentation tasks).\n",
    "\n",
    "https://pytorch.org/vision/stable/transforms.html?highlight=transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "batch_size = 4\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us show some of the training images, for fun.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  car  bird   car plane\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Define a Convolutional Neural Network\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "Copy the neural network from the Neural Networks section before and modify it to\n",
    "take 3-channel images (instead of 1-channel images as it was defined).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = torch.flatten(x, 1) # flatten all dimensions except batch\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Define a Loss function and optimizer\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Train the network\n",
    "^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "This is when things start to get interesting.\n",
    "We simply have to loop over our data iterator, and feed the inputs to the\n",
    "network and optimize.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 1.734\n",
      "2.371568202972412\n",
      "[1,  4000] loss: 1.623\n",
      "1.4074316024780273\n",
      "[1,  6000] loss: 1.569\n",
      "2.6225767135620117\n",
      "[1,  8000] loss: 1.497\n",
      "1.8314707279205322\n",
      "[1, 10000] loss: 1.454\n",
      "1.4173905849456787\n",
      "[1, 12000] loss: 1.424\n",
      "0.5971309542655945\n",
      "[2,  2000] loss: 1.356\n",
      "1.9059807062149048\n",
      "[2,  4000] loss: 1.331\n",
      "0.5194963812828064\n",
      "[2,  6000] loss: 1.329\n",
      "1.6193125247955322\n",
      "[2,  8000] loss: 1.282\n",
      "1.115276575088501\n",
      "[2, 10000] loss: 1.263\n",
      "1.193916916847229\n",
      "[2, 12000] loss: 1.261\n",
      "0.9571274518966675\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data  \n",
    "        # 这个dataloader的作用就是可以方便地直接取出inputs和labels\n",
    "        # 瞅瞅这个labels打印出来是啥样： tensor([9, 2, 9, 1])\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 2000))  # 这是为了打印2000个batch的平均loss\n",
    "            print(loss.item())  # 这是直接看每2000batch最后的loss\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's quickly save our trained model:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "PATH = './weights/cifar_net.pth'\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\n",
    "for more details on saving PyTorch models.\n",
    "\n",
    "5. Test the network on the test data\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "We have trained the network for 2 passes over the training dataset.\n",
    "But we need to check if the network has learnt anything at all.\n",
    "\n",
    "We will check this by predicting the class label that the neural network\n",
    "outputs, and checking it against the ground-truth. If the prediction is\n",
    "correct, we add the sample to the list of correct predictions.\n",
    "\n",
    "Okay, first step. Let us display an image from the test set to get familiar.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:    cat  ship  ship plane\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load back in our saved model (note: saving and re-loading the model\n",
    "wasn't necessary here, we only did it to illustrate how to do so):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "net.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let us see what the neural network thinks these examples above are:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-2.5143, -3.8691,  1.9938,  3.4914,  1.9326,  2.9498,  2.6399,  0.0725,\n",
       "         -3.1382, -3.2224],\n",
       "        [-2.0805, -4.1059,  1.6089,  2.2321,  0.5522,  4.7209, -0.3734,  1.9963,\n",
       "         -2.5493, -2.4220],\n",
       "        [ 0.6817,  2.1851, -0.4041, -0.7862,  0.2973, -0.4654, -1.0323, -0.2317,\n",
       "          0.1119,  0.0299],\n",
       "        [-1.6924, -2.8905,  1.3159, -1.1019,  4.3273,  2.3972, -1.3612,  8.3016,\n",
       "         -6.9652, -2.7508]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs = net(images)\n",
    "outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outputs are energies for the 10 classes.\n",
    "The higher the energy for a class, the more the network\n",
    "thinks that the image is of the particular class.\n",
    "So, let's get the index of the highest energy:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:    cat  ship  ship  ship\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(outputs, 1)  # axis其实就是从第几个括号里面去找最大值\n",
    "\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results seem pretty good.\n",
    "\n",
    "Let us look at how the network performs on the whole dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 57 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "# since we're not training, we don't need to calculate the gradients for our outputs\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        # calculate outputs by running images through the network \n",
    "        outputs = net(images)\n",
    "        # the class with the highest energy is what we choose as prediction\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks way better than chance, which is 10% accuracy (randomly picking\n",
    "a class out of 10 classes).\n",
    "Seems like the network learnt something.\n",
    "\n",
    "Hmmm, what are the classes that performed well, and the classes that did\n",
    "not perform well:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class plane is: 66.3 %\n",
      "Accuracy for class car   is: 70.4 %\n",
      "Accuracy for class bird  is: 37.6 %\n",
      "Accuracy for class cat   is: 22.1 %\n",
      "Accuracy for class deer  is: 52.9 %\n",
      "Accuracy for class dog   is: 67.9 %\n",
      "Accuracy for class frog  is: 65.0 %\n",
      "Accuracy for class horse is: 65.1 %\n",
      "Accuracy for class ship  is: 72.9 %\n",
      "Accuracy for class truck is: 50.1 %\n"
     ]
    }
   ],
   "source": [
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data    \n",
    "        outputs = net(images)    \n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "  \n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(\"Accuracy for class {:5s} is: {:.1f} %\".format(classname, \n",
    "                                                   accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so what next?\n",
    "\n",
    "How do we run these neural networks on the GPU?\n",
    "\n",
    "Training on GPU\n",
    "----------------\n",
    "Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
    "net onto the GPU.\n",
    "\n",
    "Let's first define our device as the first visible cuda device if we have\n",
    "CUDA available:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    {
     "name": "stdout",
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     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of this section assumes that ``device`` is a CUDA device.\n",
    "\n",
    "Then these methods will recursively go over all modules and convert their\n",
    "parameters and buffers to CUDA tensors:\n",
    "\n",
    ".. code:: python\n",
    "\n",
    "    net.to(device)\n",
    "\n",
    "\n",
    "Remember that you will have to send the inputs and targets at every step\n",
    "to the GPU too:\n",
    "\n",
    ".. code:: python\n",
    "\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "\n",
    "Why don't I notice MASSIVE speedup compared to CPU? Because your network\n",
    "is really small.\n",
    "\n",
    "**Exercise:** Try increasing the width of your network (argument 2 of\n",
    "the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\n",
    "they need to be the same number), see what kind of speedup you get.\n",
    "\n",
    "**Goals achieved**:\n",
    "\n",
    "- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
    "- Train a small neural network to classify images\n",
    "\n",
    "Training on multiple GPUs\n",
    "-------------------------\n",
    "If you want to see even more MASSIVE speedup using all of your GPUs,\n",
    "please check out :doc:`data_parallel_tutorial`.\n",
    "\n",
    "Where do I go next?\n",
    "-------------------\n",
    "\n",
    "-  :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n",
    "-  `Train a state-of-the-art ResNet network on imagenet`_\n",
    "-  `Train a face generator using Generative Adversarial Networks`_\n",
    "-  `Train a word-level language model using Recurrent LSTM networks`_\n",
    "-  `More examples`_\n",
    "-  `More tutorials`_\n",
    "-  `Discuss PyTorch on the Forums`_\n",
    "-  `Chat with other users on Slack`_\n",
    "\n",
    "\n"
   ]
  },
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